Making science more reproducible has the potential to advance scientific research and make researchers' work more effective and productive. For computational and data-intensive research, which is increasingly pervasive across the life sciences, this is particularly true, and yet is often seen as difficult to achieve. During this 2-day hackathon, researchers interested in developing teaching resources will develop new materials and refine existing lessons for the Reproducible Science curriculum. This curriculum will then be used to teach tools and practices that can be used to make one's computational science more reproducible.

The Reproducible Science Curriculum was initially developed during a hackathon [1] sponsored by NSF, hosted at NESCent in December 2014. Since then, the content developed was taught three times: at Duke (May 14-15, 2015), at iDigBio (June 1-2, 2015) [2] and at the Duke Marine Lab (September 24-25, 2015). The goals of this second hackathon is to refine the current material, and convert language specific lessons from R to Python.